Active learning for Graph Neural Networks (GNNs) aims to select valuable unlabeled samples for annotation with a limited budget to maximize the GNNs’ performance at a low cost. However, most methods often result in imbalanced class distributions, leading to a bias toward majority classes, which undermines minority class performance and overall model effectiveness. To tackle this issue, we develop the Class-Balanced Active Learning System for Graphs GraphCBAL-Sys. It learns an optimal policy through reinforcement learning to acquire class-balanced and informative nodes for annotation. Additionally, GraphCBAL-Sys is capable of visualizing the internal processes and results during our model’s training and testing phases. Our demonstration video can be found here: https://b23.tv/yCLOIPw .